Tables REF –REF  present the numerical values of the means and standard deviations of the solution errors for the 10 runs. For comparison, we also include the results reported in
[1]} when PINNs are trained with expanding the solution interval on \(t\)  using a pre-defined schedule (denoted as `schedule \(\Delta t\) ` in the tables).
One can see that the proposed method performs well on systems with different types of boundary conditions (see, e.g., Table REF ) and it outperforms the scheduling approach. Note also that in our experiments we obtain significantly better results when we train PINNs with Adam compared to the results obtained with the LBFGS optimizer in [1]}.
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